Novel Relationships Between Temperature, Kelp Abundance, and Species Richness in New England Kelp Forests

Temperature

We collected temperature from the nearest offshore buoy to each site. While these temperatures were likely not the conditions experienced precisely at the site, they should at least correlate to both regional and temporal trends experienced by kelps. As kelp reproduction and initial growth occurs in the winter and early spring, we chose to examine minimum winter temperature as a reflection of conditions that could influence both kelp abundance as well as influence the community as a whole. note to self - this justification either needs beefing up, referencing - ha, or I need to systematically test a few other summarizations of temperature. They’re all pretty correlated anyway –Note to co-authors, I also attempted this briefly with nearest NOAA monitoring station, as this pulled stations within the Narraganset and Boston Harbor, but those stations differed by upwards of 8C on minima from others, and not in directions you would suspect, leading me to think that offshore is the more appropriate way to go

While some sites had benthic loggers deployed during the period of observation, buoy data enabled a more comprehensive analysis for two reasons. First, this meant that the first year sampled had to be thrown out due to no preceeding winter temperatures recorded. This filtering was particularly problematic as a few sites only have one year of data at present. Second, For analyses to determine a proper causal linkage (see data analysis below), we required temperature data during the duration of sampling for the entire data set - e.g., even for a site sampled in 2016, our models required data from 2013-2017.

Statistical analysis (currently interdigitated with results for the whole data set)

To examine the relationship between temperature and Saccharina latissima abundance, we first took a naive look at the relationship between winter minimum temperature at kelp abundance by fitting a generalized linear mixed model with a lognormal error and log link. Percent rocky substrate was included as a covariate, and we included a random effect of site. To evaluate the model, we include a chi square log ratio test of the model as well as tests of the coefficients.

While this model seems to demonstrate a negative relationship between temperature and sugar kelp abundance, given spatial gradients of temperature that correlate with a wide variety of other drivers, it’s difficult to say whether this represents a true relationship. To assess the importance of temperature with greater confidence, we used a hierarchical model with group mean centering [@REF TBA]. By calculating a centered mean temperature from 2014-2016, an annual deviation from site-level mean, and including both in the model along with a random effect of site, we’re able to 1) derive a relationship driven by between-year variation in temperature without the confusion of spatial correlates and 2) evaluate the relationship due to spatial differences in thermal regime via use of a hierarchical predictor alongside a site-level random effect. In essence, we partition the spatial and temporal component of temperature’s effects, and the deviation from site average coefficient addresses the impact of temporal change in temperature at a site.

This model shows a roughly 11.1% decrease in kelp per degree increase in temperature at a site.

To further support the conclusion that temperature drives these kelp forests, we conducted a differencing analysis of those sites sampled throughout the entire duration of the study. For each year sampled at these sites, we calculated the change in S. latissima and compared it to the change in temperature using a linear mixed model with site as a random intercept and percent rocky cover as a covariate. To make our results comparable to previous analysis, rather than looking at difference in abundance per se, we looked at percent change, i.e. (kelp_t - kelp_t-1)/kelp_t-1. This method has the advantage of removing any signal of spatial differences - be they from a gradient or just site to site variability - and focuses instead on the dynamics of temporal change. This does restrict our sample size somewhat (n=18) relative to previous analyses, but we have fewer parameters to estimate.

Here again we see that for one degree change we have a -18.733% change in kelp.